A theory of diagnosis from first principles
Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Possible conflicts: a compilation technique for consistency-based diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper introduces a factoring method for Dynamic Bayesian Networks (DBNs) based on Possible Conflicts (PCs), which aim to reduce the computational burden of Particle Filter inference. Assuming single fault hypothesis and known fault modes, the method allows performing consistency based fault detection, isolation and identification of continuous dynamic systems, with the unifying formalism of DBNs. The three tank system benchmark has been used to illustrate the approach. Two fault scenarios are discussed and a comparison of the behaviors of a DBN of the complete system with the DBN factors is also included. Comparison has confirmed that DBN computation is more efficient for factors than for the complete DBN.